40 lines
1.0 KiB
Python
40 lines
1.0 KiB
Python
# accuracy_top-1 : 81.52 accuracy_top-5 : 95.73
|
|
_base_ = [
|
|
'../_base_/models/tnt_s_patch16_224.py',
|
|
'../_base_/datasets/imagenet_bs32_pil_resize.py',
|
|
'../_base_/default_runtime.py'
|
|
]
|
|
|
|
img_norm_cfg = dict(
|
|
mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
|
|
|
|
test_pipeline = [
|
|
dict(type='LoadImageFromFile'),
|
|
dict(
|
|
type='Resize',
|
|
size=(248, -1),
|
|
interpolation='bicubic',
|
|
backend='pillow'),
|
|
dict(type='CenterCrop', crop_size=224),
|
|
dict(type='Normalize', **img_norm_cfg),
|
|
dict(type='ImageToTensor', keys=['img']),
|
|
dict(type='Collect', keys=['img'])
|
|
]
|
|
|
|
dataset_type = 'ImageNet'
|
|
data = dict(
|
|
samples_per_gpu=32, workers_per_gpu=4, test=dict(pipeline=test_pipeline))
|
|
|
|
# optimizer
|
|
optimizer = dict(type='AdamW', lr=1e-3, weight_decay=0.05)
|
|
optimizer_config = dict(grad_clip=None)
|
|
|
|
lr_config = dict(
|
|
policy='CosineAnnealing',
|
|
min_lr=0,
|
|
warmup_by_epoch=True,
|
|
warmup='linear',
|
|
warmup_iters=5,
|
|
warmup_ratio=1e-3)
|
|
runner = dict(type='EpochBasedRunner', max_epochs=300)
|